

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Dict								=	KandinskyVaaControlnetPipeline
							UpperCAmelCase_						:		Dict								=	['''image_embeds''', '''negative_image_embeds''', '''hint''']
							UpperCAmelCase_						:		Any								=	['''image_embeds''', '''negative_image_embeds''', '''hint''']
							UpperCAmelCase_						:		Optional[Any]								=	[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCAmelCase_						:		List[str]								=	False




							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														return 32




							@property
							def       snake_case			(					self						)		->       List[Any]:
														return 32




							@property
							def       snake_case			(					self						)		->       Any:
														return self.time_input_dim




							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														return self.time_input_dim * 4




							@property
							def       snake_case			(					self						)		->       Any:
														return 1_00




							@property
							def       snake_case			(					self						)		->       List[str]:
														torch.manual_seed(0						)

														A      :					Any      		=					{
														    '''in_channels''': 8,
														    # Out channels is double in channels because predicts mean and variance
														    '''out_channels''': 8,
														    '''addition_embed_type''': '''image_hint''',
														    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
														    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
														    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
														    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
														    '''layers_per_block''': 1,
														    '''encoder_hid_dim''': self.text_embedder_hidden_size,
														    '''encoder_hid_dim_type''': '''image_proj''',
														    '''cross_attention_dim''': self.cross_attention_dim,
														    '''attention_head_dim''': 4,
														    '''resnet_time_scale_shift''': '''scale_shift''',
														    '''class_embed_type''': None,
														}

														A      :					Dict      		=					UNetaDConditionModel(**__UpperCAmelCase						)
														return model




							@property
							def       snake_case			(					self						)		->       Dict:
														return {
														    "block_out_channels": [32, 32, 64, 64],
														    "down_block_types": [
														        "DownEncoderBlock2D",
														        "DownEncoderBlock2D",
														        "DownEncoderBlock2D",
														        "AttnDownEncoderBlock2D",
														    ],
														    "in_channels": 3,
														    "latent_channels": 4,
														    "layers_per_block": 1,
														    "norm_num_groups": 8,
														    "norm_type": "spatial",
														    "num_vq_embeddings": 12,
														    "out_channels": 3,
														    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
														    "vq_embed_dim": 4,
														}




							@property
							def       snake_case			(					self						)		->       Tuple:
														torch.manual_seed(0						)
														A      :					str      		=					VQModel(**self.dummy_movq_kwargs						)
														return model




							def       snake_case			(					self						)		->       Optional[int]:
														A      :					List[Any]      		=					self.dummy_unet
														A      :					List[str]      		=					self.dummy_movq

														A      :					Union[str, Any]      		=					DDIMScheduler(
														    num_train_timesteps=10_00    ,						beta_schedule='''linear'''    ,						beta_start=0.0_0_0_8_5    ,						beta_end=0.0_1_2    ,						clip_sample=__UpperCAmelCase    ,						set_alpha_to_one=__UpperCAmelCase    ,						steps_offset=1    ,						prediction_type='''epsilon'''    ,						thresholding=__UpperCAmelCase    ,						)

														A      :					Dict      		=					{
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''movq''': movq,
														}
														return components




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=0						)		->       Any:
														A      :					int      		=					floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
														A      :					Optional[Any]      		=					floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1						)						).to(
														    __UpperCAmelCase						)

														# create hint
														A      :					Optional[Any]      		=					floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)

														if str(__UpperCAmelCase						).startswith('''mps'''						):
																					A      :					int      		=					torch.manual_seed(__UpperCAmelCase						)
														else:
																					A      :					List[str]      		=					torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
														A      :					Any      		=					{
														    '''image_embeds''': image_embeds,
														    '''negative_image_embeds''': negative_image_embeds,
														    '''hint''': hint,
														    '''generator''': generator,
														    '''height''': 64,
														    '''width''': 64,
														    '''guidance_scale''': 4.0,
														    '''num_inference_steps''': 2,
														    '''output_type''': '''np''',
														}
														return inputs






							def       snake_case			(					self						)		->       List[str]:
														A      :					int      		=					'''cpu'''

														A      :					Optional[Any]      		=					self.get_dummy_components()

														A      :					Any      		=					self.pipeline_class(**__UpperCAmelCase						)
														A      :					Optional[int]      		=					pipe.to(__UpperCAmelCase						)

														pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Union[str, Any]      		=					pipe(**self.get_dummy_inputs(__UpperCAmelCase						)						)
														A      :					str      		=					output.images

														A      :					str      		=					pipe(
														    **self.get_dummy_inputs(__UpperCAmelCase						)    ,						return_dict=__UpperCAmelCase    ,						)[0]

														A      :					Dict      		=					image[0, -3:, -3:, -1]
														A      :					Tuple      		=					image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)

														A      :					Tuple      		=					np.array(
														    [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]						)

														assert (
														    np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
														), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

														assert (
														    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2
														), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







@slow
@require_torch_gpu
class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def       snake_case			(					self						)		->       List[str]:
														A      :					str      		=					load_numpy(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''						)

														A      :					Dict      		=					load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
														    '''/kandinskyv22/hint_image_cat.png'''						)
														A      :					Any      		=					torch.from_numpy(np.array(__UpperCAmelCase						)						).float() / 2_5_5.0
														A      :					Union[str, Any]      		=					hint.permute(2    ,						0    ,						1						).unsqueeze(0						)

														A      :					Union[str, Any]      		=					KandinskyVaaPriorPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa						)
														pipe_prior.to(__UpperCAmelCase						)

														A      :					Tuple      		=					KandinskyVaaControlnetPipeline.from_pretrained(
														    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''    ,						torch_dtype=torch.floataa						)
														A      :					Tuple      		=					pipeline.to(__UpperCAmelCase						)
														pipeline.set_progress_bar_config(disable=__UpperCAmelCase						)

														A      :					Tuple      		=					'''A robot, 4k photo'''

														A      :					List[Any]      		=					torch.Generator(device='''cuda'''						).manual_seed(0						)
														A		,		A      :					Tuple      		=					pipe_prior(
														    __UpperCAmelCase    ,						generator=__UpperCAmelCase    ,						num_inference_steps=5    ,						negative_prompt=''''''    ,						).to_tuple()

														A      :					Dict      		=					torch.Generator(device='''cuda'''						).manual_seed(0						)
														A      :					Optional[Any]      		=					pipeline(
														    image_embeds=__UpperCAmelCase    ,						negative_image_embeds=__UpperCAmelCase    ,						hint=__UpperCAmelCase    ,						generator=__UpperCAmelCase    ,						num_inference_steps=1_00    ,						output_type='''np'''    ,						)

														A      :					Any      		=					output.images[0]

														assert image.shape == (5_12, 5_12, 3)

														assert_mean_pixel_difference(__UpperCAmelCase    ,						__UpperCAmelCase						)









import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_pytesseract_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=18    ,						__UpperCAmelCase=30    ,						__UpperCAmelCase=4_00    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						)		->       int:
														A      :					Optional[Any]      		=					size if size is not None else {'''height''': 18, '''width''': 18}
														A      :					Dict      		=					parent
														A      :					Any      		=					batch_size
														A      :					Dict      		=					num_channels
														A      :					Optional[Any]      		=					image_size
														A      :					Dict      		=					min_resolution
														A      :					Optional[int]      		=					max_resolution
														A      :					Any      		=					do_resize
														A      :					List[Any]      		=					size
														A      :					List[Any]      		=					apply_ocr






							def       snake_case			(					self						)		->       Tuple:
														return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Union[str, Any]								=	LayoutLMvaImageProcessor if is_pytesseract_available() else None




							def       snake_case			(					self						)		->       int:
														A      :					Optional[Any]      		=					LayoutLMvaImageProcessingTester(self						)




							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														return self.image_processor_tester.prepare_image_processor_dict()




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Tuple      		=					self.image_processing_class(**self.image_processor_dict						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_resize'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''size'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''apply_ocr'''						)						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Any      		=					self.image_processing_class.from_dict(self.image_processor_dict						)
														self.assertEqual(image_processor.size    ,						{'''height''': 18, '''width''': 18}						)

														A      :					int      		=					self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42						)
														self.assertEqual(image_processor.size    ,						{'''height''': 42, '''width''': 42}						)




							def       snake_case			(					self						)		->       List[str]:
														pass




							def       snake_case			(					self						)		->       List[str]:
														# Initialize image_processing
														A      :					Optional[Any]      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PIL images
														A      :					Union[str, Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						Image.Image						)

														# Test not batched input
														A      :					str      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						)
														self.assertEqual(
														    encoding.pixel_values.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)

														self.assertIsInstance(encoding.words    ,						__UpperCAmelCase						)
														self.assertIsInstance(encoding.boxes    ,						__UpperCAmelCase						)

														# Test batched
														A      :					int      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														# Initialize image_processing
														A      :					Any      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random numpy tensors
														A      :					Optional[Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						numpify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						np.ndarray						)

														# Test not batched input
														A      :					Dict      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)

														# Test batched
														A      :					int      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       Optional[int]:
														# Initialize image_processing
														A      :					Optional[Any]      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														A      :					Optional[Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						torchify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						torch.Tensor						)

														# Test not batched input
														A      :					List[str]      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)

														# Test batched
														A      :					Dict      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)






							def       snake_case			(					self						)		->       Any:
														# with apply_OCR = True
														A      :					Dict      		=					LayoutLMvaImageProcessor()

														from datasets import load_dataset

														A      :					Dict      		=					load_dataset('''hf-internal-testing/fixtures_docvqa'''    ,						split='''test'''						)

														A      :					Any      		=					Image.open(ds[0]['''file''']						).convert('''RGB'''						)

														A      :					List[Any]      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						)

														self.assertEqual(encoding.pixel_values.shape    ,						(1, 3, 2_24, 2_24)						)
														self.assertEqual(len(encoding.words						)    ,						len(encoding.boxes						)						)

														# fmt: off
														# the words and boxes were obtained with Tesseract 4.1.1
														A      :					Tuple      		=					[['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
														A      :					int      		=					[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
														# fmt: on

														self.assertListEqual(encoding.words    ,						__UpperCAmelCase						)
														self.assertListEqual(encoding.boxes    ,						__UpperCAmelCase						)

														# with apply_OCR = False
														A      :					Union[str, Any]      		=					LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase						)

														A      :					List[str]      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						)

														self.assertEqual(encoding.pixel_values.shape    ,						(1, 3, 2_24, 2_24)						)






